CNNs were originally proposed for performing classification and regression tasks on image, video like data but lately are even being used for generic inputs such as mesh data which is crucial to many scientific applications. In particular, U-Net like architectures have shown great potential in learning on large-scale inputs and lately have been successfully used for simulating mechanical responses of materials as well [1]}, [2]}. The name U-Net comes from the particular U-shaped architecture which involves a series of convolutional and pooling operations. Convolutional layers are responsible for non-linear transformations whereas pooling enables learning through low-fidelity representation thus making the network capable of learning on high-dimensional inputs. Experiments presented in this work are carried out by using the CNN U-Net framework proposed by [2]}.
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